Each of these is unit tested, as described below, and in addition there is a driver script, api_driver.sql, that calls each of them and lists the results of logging and code timing.

I presented on Writing Clean Code in PL/SQL and SQL at the Ireland Oracle User Group Conference on 4 April 2019 in Dublin. The modules demonstrated here are written in the style recommended in the presentation where, in particular:

‘functional’ code is preferred

object-oriented code is used only where necessary, using a package record array approach, rather than type bodies

record types, defaults and overloading used extensively to provide clean API interfaces

Screen Recordings on this Module

I initially made a series of screen recordings that are available at the links below, and later condensed each recording to a length that would upload directly to Twitter, i.e. less than 140 seconds. You can find the Twitter thread here. Both sets of recordings are also available in the recordings subfolder of the repository. The links below are to the initial, longer set of recordings.

4 Running the scripts (4 recordings – 30m)

Unit Testing

The PL/SQL APIs are tested using the Math Function Unit Testing design pattern, with test results in HTML and text format included. The design pattern is based on the idea that all API testing programs can follow a universal design pattern, using the concept of a ‘pure’ function as a wrapper to manage the ‘impurity’ inherent in database APIs. I explained the concepts involved in a presentation at the Ireland Oracle User Group Conference in March 2018:

In this data-driven design pattern a driver program reads a set of scenarios from a JSON file, and loops over the scenarios calling the wrapper function with the scenario as input and obtaining the results as the return value. Utility functions from the Trapit module convert the input JSON into PL/SQL arrays, and, conversely, the output arrays into JSON text that is written to an output JSON file. This latter file contains all the input values and output values (expected and actual), as well as metadata describing the input and output groups. A separate nodejs module can be run to process the output files and create HTML files showing the results: Each unit test (say `pkg.prc`) has its own root page `pkg.prc.html` with links to a page for each scenario, located within a subfolder `pkg.prc`. Here, they have been copied into a subfolder test_output, as follows:

tt_emp_batch.load_emps

tt_emp_ws.get_dept_emps

tt_emp_ws.save_emps

tt_view_drivers.hr_test_view_v

Where the actual output record matches expected, just one is represented, while if the actual differs it is listed below the expected and with background colour red. The employee group in scenario 4 of tt_emp_ws.save_emps has two records deliberately not matching, the first by changing the expected salary and the second by adding a duplicate expected record.

Each of the `pkg.prc` subfolders also includes a JSON Structure Diagram, `pkg.prc.png`, showing the input/output structure of the pure unit test wrapper function. For example:

Running a test causes the actual values to be inserted to the JSON object, which is then formatted as HTML pages:

Here is the output JSON for the 4’th scenario of the corresponding test:

Here are images of the unit test summary and 4’th scenario pages for the corresponding test:

Logging and Instrumentation

Program instrumentation means including lines of code to monitor the execution of a program, such as tracing lines covered, numbers of records processed, and timing information. Logging means storing such information, in database tables or elsewhere.

The Log_Set module allows for logging of various data in a lines table linked to a header for a given log, with the logging level configurable at runtime. The module also uses Oracle’s DBMS_Application_Info API to allow for logging in memory only with information accessible via the V$SESSION and V$SESSION_LONGOPS views.

The two web service-type APIs, Emp_WS.Save_Emps and Emp_WS.Get_Dept_Emps, use a configuration that logs only via DBMS_Application_Info, while the batch API, Emp_Batch.Load_Emps, also logs to the tables. The view of course does not do any logging itself but calling programs can log the results of querying it.

The driver script api_driver.sql calls all four of the demo APIs and performs its own logging of the calls and the results returned, including the DBMS_Application_Info on exit. The driver logs using a special DEBUG configuration where the log is constructed implicitly by the first Put, and there is no need to pass a log identifier when putting (so debug lines can be easily added in any called package). At the end of the script queries are run that list the contents of the logs created during the session in creation order, first normal logs, then a listing for error logs (of which one is created by deliberately raising an exception handled in WHEN OTHERS).

Here, for example, is the text logged by the driver script for the first call:

Functional PL/SQL

The recordings 1.5 and 1.6 show examples of the functional style of PL/SQL used in the utility packages demonstrated, and here is a diagram from 1.6 illustrating a design pattern identified in refactoring the main subprogram of the unit test programs.

Github Desktop

In order to clone the code as a git repository you need to have the git application installed. I recommend Github Desktop UI for managing repositories on windows. This depends on the git application, available here: git downloads, but can also be installed from within Github Desktop, according to these instructions:How to install GitHub Desktop.

nodejs (Javascript backend)

nodejs is needed to run a program that turns the unit test output files into formatted HTML pages. It requires no javascript knowledge to run the program, and nodejs can be installed here.

Install 2: Clone git repository

The following steps will download the repository into a folder, oracle_plsql_api_demos, within your GitHub root folder:

Open Github desktop and click [File/Clone repository…]

Paste into the url field on the URL tab: https://github.com/BrenPatF/oracle_plsql_api_demos.git

Choose local path as folder where you want your GitHub root to be

Click [Clone]

Install 3: Install pre-requisite modules

The demo install depends on the pre-requisite modules Utils, Trapit, Log_Set, and Timer_Set, and `lib` and `app` schemas refer to the schemas in which Utils and examples are installed, respectively.

The pre-requisite modules can be installed by following the instructions for each module at the module root pages listed in the `See also` section below. This allows inclusion of the examples and unit tests for those modules. Alternatively, the next section shows how to install these modules directly without their examples or unit tests here.

install_sys.sql creates an Oracle directory, `input_dir`, pointing to ‘c:\input’. Update this if necessary to a folder on the database server with read/write access for the Oracle OS user

Run script from slqplus:

SQL> @install_sys

[Schema: lib; Folder: install_prereq\lib] Create lib components

Run script from slqplus:

SQL> @install_lib_all

[Schema: app; Folder: install_prereq\app] Create app synonyms

Run script from slqplus:

SQL> @c_syns_all

[Folder: (npm root)] Install npm trapit package

The npm trapit package is a nodejs package used to format unit test results as HTML pages.

Open a DOS or Powershell window in the folder where you want to install npm packages, and, with nodejs installed, run:

$ npm install trapit

This should install the trapit nodejs package in a subfolder .\node_modules\trapit

Install 4: Create Oracle PL/SQL API Demos components

[Folder: (root)]

Copy the following files from the root folder to the server folder pointed to by the Oracle directory INPUT_DIR:

tt_emp_ws.save_emps_inp.json

tt_emp_ws.get_dept_emps_inp.json

tt_emp_batch.load_emps_inp.json

tt_view_drivers.hr_test_view_v_inp.json

There is also a bash script to do this, assuming C:\input as INPUT_DIR:

$ ./cp_json_to_input.sh

[Schema: lib; Folder: lib]

Run script from slqplus:

SQL> @install_jobs app

[Schema: hr; Folder: hr]

Run script from slqplus:

SQL> @install_hr app

[Schema: app; Folder: app]

Run script from slqplus:

SQL> @install_api_demos lib

Running Driver Script and Unit Tests

Running driver script

[Schema: app; Folder: app]

Run script from slqplus:

SQL> @api_driver

The output is in api_driver.log

Running unit tests

[Schema: app; Folder: app]

Run script from slqplus:

SQL> @r_tests

Testing is data-driven from the input JSON objects that are loaded from files into the table tt_units (at install time), and produces JSON output files in the INPUT_DIR folder, that contain arrays of expected and actual records by group and scenario. These files are:

tt_emp_batch.load_emps_out.json

tt_emp_ws.get_dept_emps_out.json

tt_emp_ws.save_emps_out.json

tt_view_drivers.hr_test_view_v_out.json

The output files are processed by a nodejs program that has to be installed separately, from the `npm` nodejs repository, as described in the Installation section above. The nodejs program produces listings of the results in HTML and/or text format, and result files are included in the subfolders below test_output. To run the processor (in Windows), open a DOS or Powershell window in the trapit package folder after placing the output JSON files in the subfolder ./examples/externals and run:

This is H1

This is H2

This is strongThis is em

Last October I gave a presentation on database unit testing with utPLSQL, Oracle Unit Testing with utPLSQL. I mentioned design patterns as a way of reducing the effort of building unit tests and outlined some strategies for coding them effectively.

In the current set of articles, I develop the ideas further, starting from the idea that all database APIs can be considered in terms of the axes:

direction (i.e. getter or setter, noting that setters can also ‘get’)

mode (i.e. real time or batch)

For each cell in the implied matrix, I construct an example API (or view) with specified requirements against Oracle’s HR demo schema, and use this example to construct a testing program with appropriate scenarios as a design pattern. Concepts and common patterns and anti-patterns in automated API testing are discussed throughout, and these are largely independent of testing framework used. However, the examples use my own lightweight independent framework that is designed to help avoid many API testing anti-patterns. The code is available on GitHub here, and includes both framework and design pattern examples: BrenPatF/db_unit_test
In this first example, I present a design pattern for web service ‘save’ procedures by means of a conceptual discussion, together with a working example of base code and unit test code for a procedure to save new employees in Oracle’s well-known HR demonstration schema. The working example can be used as a template for real use cases, and I believe can simplify the development process. It can also be used as a basis for comparing other unit testing frameworks, by implementing the same testing in those frameworks.

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This article provides the SQL that does this, and also a PL/SQL package containing a pipelined function that applies a slightly different algorithm; the latter is also practical, although it proved less efficient on my test problems.

Problem Definition

The calculation can be expressed as an additive combination of products

Within each product, each number occurs once with a power between -P and +P, including zero

All possible products will be considered

Each element in the combination has a coefficient between -C and +C, including zero

The combination has a fixed number of elements, E

The numbers are entered into a table, and a fixed number of them, N, are to be considered

The queries are to be generic, parametrised by P, C, E, N, and target value T

Integer division is messy, so I use real numbers and exclude non-integral complete products so that the order doesn’t matter

(Added 070813:)Optionally, a number can appear in at most one combination, using the BitAnd idea I borrrowed from Stew’s solution

Test Problems

I used two test problems.

Test Problem 1: Brazilian League
The first …

Note that …

SQL Solution with Recursive Subquery Factoring

SQL

Note that currently I have retained the fantasy league table and column names, but they could as well be the generic items and categories in place of players and teams: This is a generic solution.

How It Works

The solution approach is based on the method used to provide exact solutions for knapsack problems in my earlier article, but with a number of extensions to cater for the new category constraints, and to reduce searching to manageable proportions.

Results

Test Problem 1: Brazilian League

The pipelined function solved this in 5 seconds, while the SQL solution solved it in 21 seconds. The solutions were identical, as follows:

Conclusions

My idea for using recursive subquery factoring to solve combinatorial optimisation problems, such as knapsack problems, described in other articles on my blog, was previously only practical for small problems. The extensions described here render it a practical proposition even for larger problems. It is also relatively simple compared with procedural approaches.

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Dimensions (approximately): H 86cm

Key Features:
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